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Papers on “autonomous vehicles self-driving safety”

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1. A survey of deep learning techniques for autonomous driving

   Sorin Mihai Grigorescu, Bogdan Trăsnea, Tiberiu Cocias, et al. · 2019 · Journal of Field Robotics · 1,794 cites

   Abstract The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each

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2. Planning and Decision-Making for Autonomous Vehicles

   Wilko Schwarting, Javier Alonso–Mora, Daniela L. Rus · 2018 · Annual Review of Control Robotics and Autonomous Systems · 962 cites

   In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and system-compliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, su

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3. Can You Trust Autonomous Vehicles : Contactless Attacks against Sensors of Self-driving Vehicle

   Jian-Hao Liu · 2016 · 367 cites

4. Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability?

   Nidhi Kalra, Susan M. Paddock · 2016 · RAND Corporation eBooks · 305 cites

   How safe are autonomous vehicles? The answer is crucial for developing sound policies to govern their deployment. This report evaluates whether it is practical to test-drive autonomous vehicles to assess their safety. We find that it would take hundreds of millions of miles and sometimes hundreds of billions of miles of test-driving to provide clear statistical evidence of autonomous vehicle safety, suggesting it is not a practical approach.

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5. Safety of Autonomous Vehicles

   Jun Wang, Li Zhang, Yanjun Huang, et al. · 2020 · Journal of Advanced Transportation · 260 cites

   Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive engineering; however, safety still remains the key challenge for the development and commercialization of the AVs. Therefore, a comprehensive understanding of the development status of AVs and reported accidents is becoming urgent. In this article, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes. Additionally, the public on-road AV accident reports are statistically analyzed. The results show that over 3.7 million miles have been tested for A

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6. A Safety Standard Approach for Fully Autonomous Vehicles

   Philip John Koopman, Uma D. Ferrell, Frank Fratrik, et al. · 2019 · Lecture notes in computer science · 102 cites

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7. Steering towards safe self-driving laboratories

   Shi Xuan Leong, Caleb E Griesbach, Rui Zhang, et al. · 2025 · Nature Reviews Chemistry · 57 cites

   The past decade has witnessed remarkable advancements in autonomous systems, such as automobiles that are evolving from traditional vehicles to ones capable of navigating complex environments without human intervention. Similarly, the rise of self-driving laboratories (SDLs), which leverage robotics and artificial intelligence to accelerate discovery, is driving a paradigm shift in scientific research. As SDLs evolve to expand the scope of chemical processes that can be performed, it is essential to bring safety to the forefront to ensure that the necessary safeguards are in place to mitigate against potential accidents that range from near-misses to catastrophic failures. This Perspective e

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8. Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review

   Zaid Tahir, Rob Alexander · 2020 · IEEE International Conference on Artificial Intelligence Testing (AITest) · 45 cites

   Self-driving Autonomous Vehicles (SAVs) are gaining more interest each passing day by the industry as well as the general public. Tech and automobile companies are investing huge amounts of capital in research and development of SAVs to make sure they have a head start in the SAV market in the future. One of the major hurdles in the way of SAVs making it to the public roads is the lack of confidence of public in the safety aspect of SAVs. In order to assure safety and provide confidence to the public in the safety of SAVs, researchers around the world have used coverage-based testing for Verification and Validation (V&V) and safety assurance of SAVs. The objective of this paper is to investi

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